{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "ruled-projector",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scikitplot as skplt\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pymongo\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "recognized-wagon",
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_to_one_hot(sour_labels)->list:\n",
    "    sour_labels = np.array(sour_labels, dtype=np.int)\n",
    "    labels_one_hot = np.zeros((6, sour_labels.shape[1]))\n",
    "    for i in range(sour_labels.shape[1]):\n",
    "        label_slices = sour_labels[:,i]\n",
    "        for label in label_slices:\n",
    "            label = label - 1\n",
    "            if label != -1:\n",
    "                labels_one_hot[label, i] = 1\n",
    "    return labels_one_hot.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "standing-violation",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_dir =  \"/gpfs/scratch/chgwang/XI/DataBase/Model_figure\"\n",
    "df_file_path = os.path.join(df_dir, \"evalution_index_theta.csv\")\n",
    "df = pd.read_csv(df_file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "terminal-amber",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn\n",
    "seaborn.set_style(\"white\")\n",
    "channel_list = np.unique(df.loc[:,\"index\"])\n",
    "theta_list = np.unique(df.loc[:,\"theta\"])\n",
    "plt.figure()\n",
    "# markers = [\"b\",\"g\",\"c\",\"m\",\"y\",\"k\"]\n",
    "for channel in channel_list:\n",
    "    channel_eval_list = []\n",
    "    for theta in theta_list:\n",
    "        theta_index = df.loc[:,\"theta\"] == theta\n",
    "        channel_index = df.loc[:,\"index\"] == channel\n",
    "        indices = theta_index & channel_index\n",
    "        TPR = df.loc[:,\"Recall\"][indices]\n",
    "        FPR = df.loc[:, \"FPR\"][indices]\n",
    "        channel_eval_list.append([TPR, FPR])\n",
    "    channel_eval_arr = np.array(channel_eval_list, dtype=np.float32)\n",
    "    channel_eval_arr = np.squeeze(channel_eval_arr)\n",
    "    channel_eval_arr = np.append(channel_eval_arr, [[0,0]], axis=0)\n",
    "    plt.plot(channel_eval_arr[:,1], channel_eval_arr[:,0], label=channel+1)\n",
    "plt.plot([0,1],[0,1], \"r--\")    \n",
    "model_figure_dir = \"/gpfs/scratch/chgwang/XI/DataBase/Model_figure\"\n",
    "fig_path = os.path.join(model_figure_dir, \"ROC_channels.png\"%channel)\n",
    "plt.savefig(fig_path)\n",
    "plt.show()\n",
    "plt.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "funded-serial",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(62, 2)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "channel_eval_arr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "delayed-traffic",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 1.        ],\n",
       "       [0.98816198, 0.45322698],\n",
       "       [0.98685884, 0.36877829],\n",
       "       [0.98585403, 0.31845689],\n",
       "       [0.9852103 , 0.28365019],\n",
       "       [0.98475498, 0.261035  ],\n",
       "       [0.98437822, 0.2473525 ],\n",
       "       [0.98411131, 0.2344932 ],\n",
       "       [0.98371875, 0.22147147],\n",
       "       [0.98329484, 0.20774385],\n",
       "       [0.98285526, 0.19170985],\n",
       "       [0.98241568, 0.17707568],\n",
       "       [0.98191327, 0.1627907 ],\n",
       "       [0.98152071, 0.15079366],\n",
       "       [0.98120672, 0.145     ],\n",
       "       [0.98097122, 0.14042553],\n",
       "       [0.9807514 , 0.13234253],\n",
       "       [0.98034322, 0.12447552],\n",
       "       [0.9801234 , 0.12083333],\n",
       "       [0.97990358, 0.11724138],\n",
       "       [0.97963667, 0.11407104],\n",
       "       [0.97946399, 0.11261872],\n",
       "       [0.97929126, 0.11058665],\n",
       "       [0.97918141, 0.10946944],\n",
       "       [0.97905576, 0.10888444],\n",
       "       [0.97888309, 0.10571808],\n",
       "       [0.9787575 , 0.10397351],\n",
       "       [0.97850627, 0.10170604],\n",
       "       [0.97836494, 0.09934641],\n",
       "       [0.97814518, 0.09375   ],\n",
       "       [0.97789395, 0.08985133],\n",
       "       [0.97765845, 0.08664955],\n",
       "       [0.97743863, 0.08120205],\n",
       "       [0.97725022, 0.08      ],\n",
       "       [0.97699904, 0.07454201],\n",
       "       [0.97671646, 0.07196496],\n",
       "       [0.97643381, 0.06712244],\n",
       "       [0.97611982, 0.06284658],\n",
       "       [0.97589999, 0.06001225],\n",
       "       [0.97568023, 0.05721242],\n",
       "       [0.97531909, 0.05358218],\n",
       "       [0.97502083, 0.04958184],\n",
       "       [0.97464401, 0.04494382],\n",
       "       [0.97411019, 0.03960396],\n",
       "       [0.97389036, 0.03761574],\n",
       "       [0.97346646, 0.03373356],\n",
       "       [0.97307396, 0.03107345],\n",
       "       [0.97266573, 0.02900167],\n",
       "       [0.97222614, 0.02641717],\n",
       "       [0.97183365, 0.02393906],\n",
       "       [0.97148824, 0.02207862],\n",
       "       [0.97126842, 0.02139037],\n",
       "       [0.9708131 , 0.02054795],\n",
       "       [0.97031069, 0.02020725],\n",
       "       [0.96963561, 0.01976685],\n",
       "       [0.96877205, 0.01874692],\n",
       "       [0.96781439, 0.01819923],\n",
       "       [0.96655834, 0.0166205 ],\n",
       "       [0.96475279, 0.01448639],\n",
       "       [0.96059221, 0.00986193],\n",
       "       [0.33289373, 0.        ],\n",
       "       [0.        , 0.        ],\n",
       "       [0.        , 0.        ]])"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.append(channel_eval_arr, [[0,0]], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "sublime-phoenix",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1.        ],\n",
       "        [1.        ]],\n",
       "\n",
       "       [[0.00349826],\n",
       "        [0.9825421 ]],\n",
       "\n",
       "       [[0.00320105],\n",
       "        [0.98029245]],\n",
       "\n",
       "       [[0.00299155],\n",
       "        [0.97895191]],\n",
       "\n",
       "       [[0.00279666],\n",
       "        [0.97747269]],\n",
       "\n",
       "       [[0.00267972],\n",
       "        [0.97644031]],\n",
       "\n",
       "       [[0.00255305],\n",
       "        [0.97511518]],\n",
       "\n",
       "       [[0.00235816],\n",
       "        [0.97345105]],\n",
       "\n",
       "       [[0.00216327],\n",
       "        [0.97135549]],\n",
       "\n",
       "       [[0.00194889],\n",
       "        [0.96827378]]])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "channel_arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "reported-bargain",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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